Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations9357
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory268.4 B

Variable types

DateTime1
Categorical3
Numeric11

Alerts

FakeColumn has constant value "15.06" Constant
T has a high cardinality: 437 distinct values High cardinality
C6H6(GT) is highly overall correlated with CO(GT) and 5 other fieldsHigh correlation
CO(GT) is highly overall correlated with C6H6(GT) and 6 other fieldsHigh correlation
NO2(GT) is highly overall correlated with CO(GT) and 2 other fieldsHigh correlation
NOx_GT is highly overall correlated with CO(GT) and 4 other fieldsHigh correlation
PT08.S1(CO) is highly overall correlated with C6H6(GT) and 6 other fieldsHigh correlation
PT08.S2(NMHC) is highly overall correlated with C6H6(GT) and 5 other fieldsHigh correlation
PT08.S3(NOx) is highly overall correlated with C6H6(GT) and 6 other fieldsHigh correlation
PT08.S4(NO2) is highly overall correlated with C6H6(GT) and 3 other fieldsHigh correlation
PT08.S5(O3) is highly overall correlated with C6H6(GT) and 6 other fieldsHigh correlation
Time is uniformly distributed Uniform

Reproduction

Analysis started2025-06-15 12:58:30.356337
Analysis finished2025-06-15 12:58:47.604001
Duration17.25 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Date
Date

Distinct391
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size404.2 KiB
Minimum2004-01-04 00:00:00
Maximum2005-12-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-15T14:58:47.744446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:47.908551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Categorical

Uniform 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size925.1 KiB
18.00.00
 
390
05.00.00
 
390
14.00.00
 
390
13.00.00
 
390
12.00.00
 
390
Other values (19)
7407 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters74856
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18.00.00
2nd row19.00.00
3rd row20.00.00
4th row21.00.00
5th row22.00.00

Common Values

ValueCountFrequency (%)
18.00.00 390
 
4.2%
05.00.00 390
 
4.2%
14.00.00 390
 
4.2%
13.00.00 390
 
4.2%
12.00.00 390
 
4.2%
11.00.00 390
 
4.2%
10.00.00 390
 
4.2%
09.00.00 390
 
4.2%
08.00.00 390
 
4.2%
07.00.00 390
 
4.2%
Other values (14) 5457
58.3%

Length

2025-06-15T14:58:48.070091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18.00.00 390
 
4.2%
06.00.00 390
 
4.2%
20.00.00 390
 
4.2%
21.00.00 390
 
4.2%
22.00.00 390
 
4.2%
23.00.00 390
 
4.2%
00.00.00 390
 
4.2%
01.00.00 390
 
4.2%
02.00.00 390
 
4.2%
03.00.00 390
 
4.2%
Other values (14) 5457
58.3%

Most occurring characters

ValueCountFrequency (%)
0 42498
56.8%
. 18714
25.0%
1 5067
 
6.8%
2 2730
 
3.6%
3 1170
 
1.6%
8 780
 
1.0%
4 780
 
1.0%
9 780
 
1.0%
5 779
 
1.0%
7 779
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 42498
56.8%
. 18714
25.0%
1 5067
 
6.8%
2 2730
 
3.6%
3 1170
 
1.6%
8 780
 
1.0%
4 780
 
1.0%
9 780
 
1.0%
5 779
 
1.0%
7 779
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 42498
56.8%
. 18714
25.0%
1 5067
 
6.8%
2 2730
 
3.6%
3 1170
 
1.6%
8 780
 
1.0%
4 780
 
1.0%
9 780
 
1.0%
5 779
 
1.0%
7 779
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 42498
56.8%
. 18714
25.0%
1 5067
 
6.8%
2 2730
 
3.6%
3 1170
 
1.6%
8 780
 
1.0%
4 780
 
1.0%
9 780
 
1.0%
5 779
 
1.0%
7 779
 
1.0%

CO(GT)
Real number (ℝ)

High correlation 

Distinct97
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-34.207524
Minimum-200
Maximum11.9
Zeros0
Zeros (%)0.0%
Negative1683
Negative (%)18.0%
Memory size404.2 KiB
2025-06-15T14:58:48.200304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q10.6
median1.5
Q32.6
95-th percentile4.7
Maximum11.9
Range211.9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation77.65717
Coefficient of variation (CV)-2.2701781
Kurtosis0.77830552
Mean-34.207524
Median Absolute Deviation (MAD)1
Skewness-1.6661795
Sum-320079.8
Variance6030.6361
MonotonicityNot monotonic
2025-06-15T14:58:48.358781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 1683
 
18.0%
1 305
 
3.3%
1.4 279
 
3.0%
1.6 275
 
2.9%
1.5 273
 
2.9%
1.1 262
 
2.8%
0.7 260
 
2.8%
1.7 258
 
2.8%
1.3 253
 
2.7%
0.8 251
 
2.7%
Other values (87) 5258
56.2%
ValueCountFrequency (%)
-200 1683
18.0%
0.1 33
 
0.4%
0.2 45
 
0.5%
0.3 98
 
1.0%
0.4 160
 
1.7%
0.5 217
 
2.3%
0.6 244
 
2.6%
0.7 260
 
2.8%
0.8 251
 
2.7%
0.9 248
 
2.7%
ValueCountFrequency (%)
11.9 1
< 0.1%
11.5 1
< 0.1%
10.2 2
< 0.1%
10.1 1
< 0.1%
9.9 1
< 0.1%
9.5 1
< 0.1%
9.4 1
< 0.1%
9.3 1
< 0.1%
9.2 1
< 0.1%
9.1 2
< 0.1%

PT08.S1(CO)
Real number (ℝ)

High correlation 

Distinct1042
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1048.9901
Minimum-200
Maximum2040
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size404.2 KiB
2025-06-15T14:58:48.543911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile746
Q1921
median1053
Q31221
95-th percentile1502
Maximum2040
Range2240
Interquartile range (IQR)300

Descriptive statistics

Standard deviation329.83271
Coefficient of variation (CV)0.31442882
Kurtosis5.8369357
Mean1048.9901
Median Absolute Deviation (MAD)147
Skewness-1.7215034
Sum9815400
Variance108789.62
MonotonicityNot monotonic
2025-06-15T14:58:48.700093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 366
 
3.9%
973 30
 
0.3%
1100 28
 
0.3%
988 26
 
0.3%
969 26
 
0.3%
925 26
 
0.3%
938 26
 
0.3%
966 25
 
0.3%
970 25
 
0.3%
984 25
 
0.3%
Other values (1032) 8754
93.6%
ValueCountFrequency (%)
-200 366
3.9%
647 1
 
< 0.1%
649 1
 
< 0.1%
655 1
 
< 0.1%
667 3
 
< 0.1%
669 1
 
< 0.1%
676 1
 
< 0.1%
678 1
 
< 0.1%
679 1
 
< 0.1%
681 1
 
< 0.1%
ValueCountFrequency (%)
2040 1
< 0.1%
2008 1
< 0.1%
1982 1
< 0.1%
1975 1
< 0.1%
1973 1
< 0.1%
1961 1
< 0.1%
1956 1
< 0.1%
1934 1
< 0.1%
1918 1
< 0.1%
1917 1
< 0.1%

NMHC(GT)
Real number (ℝ)

Distinct430
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-159.09009
Minimum-200
Maximum1189
Zeros0
Zeros (%)0.0%
Negative8443
Negative (%)90.2%
Memory size404.2 KiB
2025-06-15T14:58:48.871823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q1-200
median-200
Q3-200
95-th percentile144.2
Maximum1189
Range1389
Interquartile range (IQR)0

Descriptive statistics

Standard deviation139.78909
Coefficient of variation (CV)-0.87867881
Kurtosis18.863824
Mean-159.09009
Median Absolute Deviation (MAD)0
Skewness4.0757845
Sum-1488606
Variance19540.99
MonotonicityNot monotonic
2025-06-15T14:58:49.018471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 8443
90.2%
66 14
 
0.1%
40 9
 
0.1%
29 9
 
0.1%
88 8
 
0.1%
93 8
 
0.1%
84 7
 
0.1%
55 7
 
0.1%
95 7
 
0.1%
60 7
 
0.1%
Other values (420) 838
 
9.0%
ValueCountFrequency (%)
-200 8443
90.2%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
14 2
 
< 0.1%
16 1
 
< 0.1%
17 4
 
< 0.1%
18 2
 
< 0.1%
ValueCountFrequency (%)
1189 1
< 0.1%
1129 1
< 0.1%
1084 1
< 0.1%
1042 1
< 0.1%
974 1
< 0.1%
926 1
< 0.1%
899 1
< 0.1%
880 1
< 0.1%
872 1
< 0.1%
840 1
< 0.1%

C6H6(GT)
Real number (ℝ)

High correlation 

Distinct408
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8656834
Minimum-200
Maximum63.7
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size404.2 KiB
2025-06-15T14:58:49.202143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile0.7
Q14
median7.9
Q313.6
95-th percentile24.42
Maximum63.7
Range263.7
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation41.380206
Coefficient of variation (CV)22.17965
Kurtosis19.188651
Mean1.8656834
Median Absolute Deviation (MAD)4.5
Skewness-4.5087629
Sum17457.2
Variance1712.3215
MonotonicityNot monotonic
2025-06-15T14:58:49.374295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 366
 
3.9%
3.6 84
 
0.9%
2.8 82
 
0.9%
3.8 79
 
0.8%
4 78
 
0.8%
3.1 77
 
0.8%
3 76
 
0.8%
2.5 75
 
0.8%
2.9 73
 
0.8%
5.4 72
 
0.8%
Other values (398) 8295
88.7%
ValueCountFrequency (%)
-200 366
3.9%
0.1 2
 
< 0.1%
0.2 8
 
0.1%
0.3 10
 
0.1%
0.4 14
 
0.1%
0.5 20
 
0.2%
0.6 23
 
0.2%
0.7 31
 
0.3%
0.8 25
 
0.3%
0.9 25
 
0.3%
ValueCountFrequency (%)
63.7 1
< 0.1%
52.1 1
< 0.1%
50.8 1
< 0.1%
50.7 1
< 0.1%
50.6 1
< 0.1%
49.5 1
< 0.1%
49.4 1
< 0.1%
48.2 1
< 0.1%
47.7 1
< 0.1%
47.5 1
< 0.1%

PT08.S2(NMHC)
Real number (ℝ)

High correlation 

Distinct1246
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean894.59528
Minimum-200
Maximum2214
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size404.2 KiB
2025-06-15T14:58:49.545130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile471
Q1711
median895
Q31105
95-th percentile1415
Maximum2214
Range2414
Interquartile range (IQR)394

Descriptive statistics

Standard deviation342.33325
Coefficient of variation (CV)0.3826683
Kurtosis2.3700888
Mean894.59528
Median Absolute Deviation (MAD)195
Skewness-0.79343464
Sum8370728
Variance117192.06
MonotonicityNot monotonic
2025-06-15T14:58:49.706525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 366
 
3.9%
853 25
 
0.3%
880 23
 
0.2%
800 23
 
0.2%
859 23
 
0.2%
985 22
 
0.2%
783 21
 
0.2%
769 21
 
0.2%
776 21
 
0.2%
850 21
 
0.2%
Other values (1236) 8791
94.0%
ValueCountFrequency (%)
-200 366
3.9%
383 2
 
< 0.1%
387 1
 
< 0.1%
388 1
 
< 0.1%
390 2
 
< 0.1%
397 1
 
< 0.1%
399 1
 
< 0.1%
402 2
 
< 0.1%
407 2
 
< 0.1%
408 1
 
< 0.1%
ValueCountFrequency (%)
2214 1
< 0.1%
2007 1
< 0.1%
1983 1
< 0.1%
1981 1
< 0.1%
1980 1
< 0.1%
1959 1
< 0.1%
1958 1
< 0.1%
1935 1
< 0.1%
1924 1
< 0.1%
1920 1
< 0.1%

NOx_GT
Real number (ℝ)

High correlation 

Distinct926
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.61697
Minimum-200
Maximum1479
Zeros0
Zeros (%)0.0%
Negative1639
Negative (%)17.5%
Memory size404.2 KiB
2025-06-15T14:58:50.061014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q150
median141
Q3284
95-th percentile653.2
Maximum1479
Range1679
Interquartile range (IQR)234

Descriptive statistics

Standard deviation257.43387
Coefficient of variation (CV)1.5267376
Kurtosis1.5054171
Mean168.61697
Median Absolute Deviation (MAD)109
Skewness0.82523219
Sum1577749
Variance66272.196
MonotonicityNot monotonic
2025-06-15T14:58:50.220793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 1639
 
17.5%
89 41
 
0.4%
65 37
 
0.4%
93 36
 
0.4%
122 36
 
0.4%
41 36
 
0.4%
132 35
 
0.4%
95 35
 
0.4%
180 35
 
0.4%
51 34
 
0.4%
Other values (916) 7393
79.0%
ValueCountFrequency (%)
-200 1639
17.5%
2 1
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
11 4
 
< 0.1%
12 4
 
< 0.1%
ValueCountFrequency (%)
1479 1
< 0.1%
1389 2
< 0.1%
1369 1
< 0.1%
1358 1
< 0.1%
1345 1
< 0.1%
1310 1
< 0.1%
1301 1
< 0.1%
1290 1
< 0.1%
1253 1
< 0.1%
1247 1
< 0.1%

PT08.S3(NOx)
Real number (ℝ)

High correlation 

Distinct1222
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean794.99017
Minimum-200
Maximum2683
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size404.2 KiB
2025-06-15T14:58:50.375862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile410
Q1637
median794
Q3960
95-th percentile1281.2
Maximum2683
Range2883
Interquartile range (IQR)323

Descriptive statistics

Standard deviation321.99355
Coefficient of variation (CV)0.40502834
Kurtosis3.1048259
Mean794.99017
Median Absolute Deviation (MAD)161
Skewness-0.38475977
Sum7438723
Variance103679.85
MonotonicityNot monotonic
2025-06-15T14:58:50.549148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 366
 
3.9%
767 25
 
0.3%
733 25
 
0.3%
846 25
 
0.3%
765 23
 
0.2%
876 23
 
0.2%
685 22
 
0.2%
816 22
 
0.2%
891 22
 
0.2%
830 22
 
0.2%
Other values (1212) 8782
93.9%
ValueCountFrequency (%)
-200 366
3.9%
322 1
 
< 0.1%
325 2
 
< 0.1%
328 1
 
< 0.1%
330 2
 
< 0.1%
334 1
 
< 0.1%
335 1
 
< 0.1%
340 2
 
< 0.1%
341 1
 
< 0.1%
345 1
 
< 0.1%
ValueCountFrequency (%)
2683 1
< 0.1%
2559 1
< 0.1%
2542 1
< 0.1%
2331 1
< 0.1%
2327 1
< 0.1%
2318 1
< 0.1%
2294 1
< 0.1%
2121 1
< 0.1%
2095 2
< 0.1%
2081 1
< 0.1%

NO2(GT)
Real number (ℝ)

High correlation 

Distinct284
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.148873
Minimum-200
Maximum340
Zeros0
Zeros (%)0.0%
Negative1642
Negative (%)17.5%
Memory size404.2 KiB
2025-06-15T14:58:50.702417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q153
median96
Q3133
95-th percentile194
Maximum340
Range540
Interquartile range (IQR)80

Descriptive statistics

Standard deviation126.94046
Coefficient of variation (CV)2.1830252
Kurtosis0.27559907
Mean58.148873
Median Absolute Deviation (MAD)40
Skewness-1.2256296
Sum544099
Variance16113.879
MonotonicityNot monotonic
2025-06-15T14:58:50.871262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 1642
 
17.5%
97 78
 
0.8%
117 77
 
0.8%
119 77
 
0.8%
95 75
 
0.8%
101 75
 
0.8%
114 75
 
0.8%
110 74
 
0.8%
115 73
 
0.8%
116 72
 
0.8%
Other values (274) 7039
75.2%
ValueCountFrequency (%)
-200 1642
17.5%
2 1
 
< 0.1%
3 1
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
11 2
 
< 0.1%
12 2
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
340 1
< 0.1%
333 1
< 0.1%
326 1
< 0.1%
322 1
< 0.1%
312 1
< 0.1%
310 1
< 0.1%
309 1
< 0.1%
306 1
< 0.1%
301 1
< 0.1%
296 1
< 0.1%

PT08.S4(NO2)
Real number (ℝ)

High correlation 

Distinct1604
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1391.4796
Minimum-200
Maximum2775
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size404.2 KiB
2025-06-15T14:58:51.029412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile757
Q11185
median1446
Q31662
95-th percentile2020.2
Maximum2775
Range2975
Interquartile range (IQR)477

Descriptive statistics

Standard deviation467.21012
Coefficient of variation (CV)0.33576497
Kurtosis3.2670279
Mean1391.4796
Median Absolute Deviation (MAD)236
Skewness-1.2441099
Sum13020075
Variance218285.3
MonotonicityNot monotonic
2025-06-15T14:58:51.187659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 366
 
3.9%
1488 24
 
0.3%
1580 22
 
0.2%
1539 21
 
0.2%
1467 20
 
0.2%
1638 19
 
0.2%
1490 18
 
0.2%
1418 18
 
0.2%
1321 17
 
0.2%
1511 17
 
0.2%
Other values (1594) 8815
94.2%
ValueCountFrequency (%)
-200 366
3.9%
551 1
 
< 0.1%
559 1
 
< 0.1%
561 1
 
< 0.1%
579 1
 
< 0.1%
601 1
 
< 0.1%
602 1
 
< 0.1%
605 1
 
< 0.1%
621 1
 
< 0.1%
637 1
 
< 0.1%
ValueCountFrequency (%)
2775 1
< 0.1%
2746 1
< 0.1%
2691 1
< 0.1%
2684 1
< 0.1%
2679 1
< 0.1%
2667 1
< 0.1%
2665 1
< 0.1%
2662 1
< 0.1%
2643 2
< 0.1%
2641 2
< 0.1%

PT08.S5(O3)
Real number (ℝ)

High correlation 

Distinct1744
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean975.07203
Minimum-200
Maximum2523
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size404.2 KiB
2025-06-15T14:58:51.348862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile348
Q1700
median942
Q31255
95-th percentile1750
Maximum2523
Range2723
Interquartile range (IQR)555

Descriptive statistics

Standard deviation456.93818
Coefficient of variation (CV)0.46861993
Kurtosis0.63829664
Mean975.07203
Median Absolute Deviation (MAD)272
Skewness-0.03466188
Sum9123749
Variance208792.5
MonotonicityNot monotonic
2025-06-15T14:58:51.505307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 366
 
3.9%
836 20
 
0.2%
825 20
 
0.2%
826 19
 
0.2%
926 18
 
0.2%
799 17
 
0.2%
777 17
 
0.2%
949 16
 
0.2%
891 16
 
0.2%
923 16
 
0.2%
Other values (1734) 8832
94.4%
ValueCountFrequency (%)
-200 366
3.9%
221 1
 
< 0.1%
225 1
 
< 0.1%
227 1
 
< 0.1%
232 1
 
< 0.1%
252 1
 
< 0.1%
253 1
 
< 0.1%
257 1
 
< 0.1%
261 2
 
< 0.1%
262 1
 
< 0.1%
ValueCountFrequency (%)
2523 1
< 0.1%
2522 1
< 0.1%
2519 1
< 0.1%
2515 1
< 0.1%
2494 1
< 0.1%
2480 1
< 0.1%
2475 1
< 0.1%
2465 1
< 0.1%
2452 1
< 0.1%
2434 1
< 0.1%

T
Categorical

High cardinality 

Distinct437
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size369.0 KiB
-200.0
 
366
20.8
 
57
21.3
 
54
20.2
 
51
13.8
 
51
Other values (432)
8778 

Length

Max length6
Median length4
Mean length3.901571
Min length3

Characters and Unicode

Total characters36507
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.3%

Sample

1st row13.6
2nd row13.3
3rd row11.9
4th row11.0
5th row11.2

Common Values

ValueCountFrequency (%)
-200.0 366
 
3.9%
20.8 57
 
0.6%
21.3 54
 
0.6%
20.2 51
 
0.5%
13.8 51
 
0.5%
12.0 49
 
0.5%
15.6 49
 
0.5%
12.3 49
 
0.5%
16.3 48
 
0.5%
19.8 48
 
0.5%
Other values (427) 8535
91.2%

Length

2025-06-15T14:58:51.677583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
200.0 366
 
3.9%
20.8 57
 
0.6%
21.3 54
 
0.6%
20.2 51
 
0.5%
13.8 51
 
0.5%
12.0 49
 
0.5%
15.6 49
 
0.5%
12.3 49
 
0.5%
16.3 48
 
0.5%
19.8 48
 
0.5%
Other values (417) 8535
91.2%

Most occurring characters

ValueCountFrequency (%)
. 9357
25.6%
1 5366
14.7%
2 4858
13.3%
3 2911
 
8.0%
0 2851
 
7.8%
4 1952
 
5.3%
5 1855
 
5.1%
6 1798
 
4.9%
8 1788
 
4.9%
7 1729
 
4.7%
Other values (2) 2042
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 9357
25.6%
1 5366
14.7%
2 4858
13.3%
3 2911
 
8.0%
0 2851
 
7.8%
4 1952
 
5.3%
5 1855
 
5.1%
6 1798
 
4.9%
8 1788
 
4.9%
7 1729
 
4.7%
Other values (2) 2042
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 9357
25.6%
1 5366
14.7%
2 4858
13.3%
3 2911
 
8.0%
0 2851
 
7.8%
4 1952
 
5.3%
5 1855
 
5.1%
6 1798
 
4.9%
8 1788
 
4.9%
7 1729
 
4.7%
Other values (2) 2042
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 9357
25.6%
1 5366
14.7%
2 4858
13.3%
3 2911
 
8.0%
0 2851
 
7.8%
4 1952
 
5.3%
5 1855
 
5.1%
6 1798
 
4.9%
8 1788
 
4.9%
7 1729
 
4.7%
Other values (2) 2042
 
5.6%

RH
Real number (ℝ)

Distinct754
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.48538
Minimum-200
Maximum88.7
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size404.2 KiB
2025-06-15T14:58:51.816812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile15
Q134.1
median48.6
Q361.9
95-th percentile77.6
Maximum88.7
Range288.7
Interquartile range (IQR)27.8

Descriptive statistics

Standard deviation51.216145
Coefficient of variation (CV)1.2970914
Kurtosis15.764154
Mean39.48538
Median Absolute Deviation (MAD)13.9
Skewness-3.9324074
Sum369464.7
Variance2623.0935
MonotonicityNot monotonic
2025-06-15T14:58:51.990645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200 366
 
3.9%
53.1 31
 
0.3%
47.8 30
 
0.3%
57.9 30
 
0.3%
60.8 27
 
0.3%
45.9 27
 
0.3%
43.4 26
 
0.3%
50.9 26
 
0.3%
49.8 26
 
0.3%
50.1 26
 
0.3%
Other values (744) 8742
93.4%
ValueCountFrequency (%)
-200 366
3.9%
9.2 2
 
< 0.1%
9.3 1
 
< 0.1%
9.6 1
 
< 0.1%
9.8 1
 
< 0.1%
9.9 2
 
< 0.1%
10 2
 
< 0.1%
10.2 1
 
< 0.1%
10.4 1
 
< 0.1%
10.7 1
 
< 0.1%
ValueCountFrequency (%)
88.7 1
 
< 0.1%
87.2 1
 
< 0.1%
87.1 1
 
< 0.1%
87 1
 
< 0.1%
86.6 2
< 0.1%
86.5 2
< 0.1%
86 1
 
< 0.1%
85.7 3
< 0.1%
85.6 1
 
< 0.1%
85.5 1
 
< 0.1%

FakeColumn
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.7 KiB
15.06
9357 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters46785
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15.06
2nd row15.06
3rd row15.06
4th row15.06
5th row15.06

Common Values

ValueCountFrequency (%)
15.06 9357
100.0%

Length

2025-06-15T14:58:52.153774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-15T14:58:52.257336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15.06 9357
100.0%

Most occurring characters

ValueCountFrequency (%)
1 9357
20.0%
5 9357
20.0%
. 9357
20.0%
0 9357
20.0%
6 9357
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 9357
20.0%
5 9357
20.0%
. 9357
20.0%
0 9357
20.0%
6 9357
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 9357
20.0%
5 9357
20.0%
. 9357
20.0%
0 9357
20.0%
6 9357
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 9357
20.0%
5 9357
20.0%
. 9357
20.0%
0 9357
20.0%
6 9357
20.0%

Interactions

2025-06-15T14:58:45.661797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:31.343667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:32.775542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:34.117059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:35.603088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:37.149309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:38.518672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:39.937699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:41.358975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:42.900264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:44.287068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:45.804411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:31.444272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:32.890017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:34.240858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:35.727291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:37.251320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:38.653743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:40.056347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:41.454191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:43.008863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:44.400027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:45.919644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:31.555158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:33.005951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:34.379165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:35.836694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:37.374757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:38.767944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:40.176912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:41.797456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:43.137461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:44.528507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:46.053045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:31.699601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:33.126295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:34.505293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:35.971150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:37.483133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:38.913210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:40.371214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:41.937766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:43.273224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:44.669385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:46.168486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:31.822215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:33.252121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:34.632150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:36.091927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:37.611502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:39.035098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:40.497748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:42.052080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:43.395667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:44.785769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:46.296275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:32.001393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:33.374841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:34.800840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:36.201235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:37.730271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:39.168244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:40.602835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:42.171639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:43.522729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:44.900402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:46.426167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:32.153574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:33.487918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:34.947443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:36.338490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:37.857951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:39.303749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:40.731138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:42.304832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:43.652826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:45.036888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:46.552234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:32.296059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:33.616367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:35.070558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:36.461606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:37.985641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:39.427564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:40.859197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:42.429227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:43.775077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:45.153453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:46.674428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:32.414590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:33.735809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:35.201784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:36.772872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:38.153746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:39.557096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:40.984417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:42.529645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:43.903155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:45.279777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:46.990099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:32.524977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:33.855458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:35.343734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:36.887068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:38.291064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:39.684552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:41.104251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:42.659847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:44.009919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:45.386181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:47.114725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:32.653452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:33.990577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:35.476679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:37.022549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:38.410691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:39.811232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:41.234411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:42.767766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:44.152978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T14:58:45.511109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-15T14:58:52.318419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
C6H6(GT)CO(GT)NMHC(GT)NO2(GT)NOx_GTPT08.S1(CO)PT08.S2(NMHC)PT08.S3(NOx)PT08.S4(NO2)PT08.S5(O3)RHTime
C6H6(GT)1.0000.5900.0280.4570.4800.9021.000-0.6420.7770.8880.0040.281
CO(GT)0.5901.0000.1360.7700.8130.5840.590-0.5800.3050.576-0.0430.193
NMHC(GT)0.0280.1361.0000.022-0.0380.1320.0280.1610.1200.0250.0110.082
NO2(GT)0.4570.7700.0221.0000.9060.4760.457-0.5220.0610.498-0.1340.262
NOx_GT0.4800.813-0.0380.9061.0000.5070.480-0.5810.0660.5510.0580.197
PT08.S1(CO)0.9020.5840.1320.4760.5071.0000.902-0.6450.6860.9060.1950.222
PT08.S2(NMHC)1.0000.5900.0280.4570.4800.9021.000-0.6420.7770.8880.0040.257
PT08.S3(NOx)-0.642-0.5800.161-0.522-0.581-0.645-0.6421.000-0.363-0.6520.0380.176
PT08.S4(NO2)0.7770.3050.1200.0610.0660.6860.777-0.3631.0000.6100.0540.174
PT08.S5(O3)0.8880.5760.0250.4980.5510.9060.888-0.6520.6101.0000.2260.166
RH0.004-0.0430.011-0.1340.0580.1950.0040.0380.0540.2261.0000.298
Time0.2810.1930.0820.2620.1970.2220.2570.1760.1740.1660.2981.000

Missing values

2025-06-15T14:58:47.296849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-15T14:58:47.471633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx_GTPT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHFakeColumn
010/03/200418.00.002.61360.0150.011.91046.0166.01056.0113.01692.01268.013.648.915.06
110/03/200419.00.002.01292.0112.09.4955.0103.01174.092.01559.0972.013.347.715.06
210/03/200420.00.002.21402.088.09.0939.0131.01140.0114.01555.01074.011.954.015.06
310/03/200421.00.002.21376.080.09.2948.0172.01092.0122.01584.01203.011.060.015.06
410/03/200422.00.001.61272.051.06.5836.0131.01205.0116.01490.01110.011.259.615.06
510/03/200423.00.001.21197.038.04.7750.089.01337.096.01393.0949.011.259.215.06
611/03/200400.00.001.21185.031.03.6690.062.01462.077.01333.0733.011.356.815.06
711/03/200401.00.001.01136.031.03.3672.062.01453.076.01333.0730.010.760.015.06
811/03/200402.00.000.91094.024.02.3609.045.01579.060.01276.0620.010.759.715.06
911/03/200403.00.000.61010.019.01.7561.0-200.01705.0-200.01235.0501.010.360.215.06
DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx_GTPT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHFakeColumn
934704/04/200505.00.000.5888.0-200.01.3528.077.01077.053.0987.0578.010.459.915.06
934804/04/200506.00.001.11031.0-200.04.4730.0182.0760.093.01129.0905.09.563.115.06
934904/04/200507.00.004.01384.0-200.017.41221.0594.0470.0155.01600.01457.09.761.915.06
935004/04/200508.00.005.01446.0-200.022.41362.0586.0415.0174.01777.01705.013.548.915.06
935104/04/200509.00.003.91297.0-200.013.61102.0523.0507.0187.01375.01583.018.236.315.06
935204/04/200510.00.003.11314.0-200.013.51101.0472.0539.0190.01374.01729.021.929.315.06
935304/04/200511.00.002.41163.0-200.011.41027.0353.0604.0179.01264.01269.024.323.715.06
935404/04/200512.00.002.41142.0-200.012.41063.0293.0603.0175.01241.01092.026.918.315.06
935504/04/200513.00.002.11003.0-200.09.5961.0235.0702.0156.01041.0770.028.313.515.06
935604/04/200514.00.002.21071.0-200.011.91047.0265.0654.0168.01129.0816.028.513.115.06